From Inverse Optimization to Feasibility to ERM
Authors: Saurabh Kumar Mishra, Anant Raj, Sharan Vaswani
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally validate our approach on synthetic and real-world problems, and demonstrate improved performance compared to existing methods. |
| Researcher Affiliation | Academia | Saurabh Mishra 1 Anant Raj 2 Sharan Vaswani 1 1Simon Fraser University 2SIERRA Project Team (Inria), Coordinated Science Laboratory (CSL), UIUC. |
| Pseudocode | Yes | Algorithm 1 for CILP Input: A, b, Training dataset D (zi, x i )N i=1, Model fθ Initialize θ1 for t = 1, 2, .., T do ˆci = fθt(zi), i [N] for i = 1, 2, .., N do qi = PCi(ˆci) by solving the optimization problem in Eq. (3) end θt+1 = arg minθ 1 2N PN i=1 ||qi fθ(zi)||2 end Output: θT +1 |
| Open Source Code | Yes | 1The code is available here |
| Open Datasets | Yes | We consider two real-world tasks (Vlastelica et al., 2019) Warcraft Shortest Path and Perfect Matching below and defer the synthetic experiments to Appendix C. |
| Dataset Splits | Yes | Both datasets consist of 10000 training samples, 1000 validation samples and 1000 test samples each. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, processors, or memory were mentioned for the experimental setup. |
| Software Dependencies | No | The paper mentions using 'CVXPY library (Diamond & Boyd, 2016)', 'ECOS solver (Domahidi et al., 2013)', and 'OSQP solver (Stellato et al., 2020)' but does not specify their version numbers. |
| Experiment Setup | Yes | We train all the methods for 50 epochs with a batch size of 100. We employ a grid search to find the best constant step size in {0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005}, across both the Adam and Adagrad optimizers. |